• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.79-82
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• 1991

Digital FHR monitoring system based on the personal computer combined wi th the digital signal processing (DSP) board was implemented. The DSP board acquires and digitally processes ultrasound fetal Doppler signal for digital rectification, envelope detection, autocorrealtion function calculation and its peak position detection. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data taransmission and on-line analysis of fetal heart rate change including on-line warning system, base-line estmation, acceleration, deceleration and variability. The advantages of PC-DSP based system can be summarized as follows: 1) Minimum hardware realization. 2) Increased flexibility. 3) Total care system.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.463-464
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• 2007

This study describes a method of a human sensibility evaluation using photoplethysmogram(PPG) signal and a biofeedback algorithm to enhance the sensibility For this objective, the heart rate variability(HRV) is extracted from the PPG signal and using the HRV and its FFT the human sensibility is evaluated. The biofeedback algorithm is designed with motion image player interacting with the results of sensibility evaluation. The sensibility evaluation test showed feasibility of the designed method.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.663-665
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• 1998

The purpose of this paper is development of portable cardiovascular signal processing device. Beat-to-beat fluctuation in heart rate, respiration, and blood pressure have been known to be meditated by autonomic nervous system activities convergent on various effector organs. This system consists of data acquisition module, main data processing module and graphic LCD module. The data acquisition module is developed for data aquisition, data multiplexing and interfacing with main data processing system. And, main data processing module is developed for data storing, data processing and interfacing with graphic LCD module. The main processing system is based on 32-bit microprocessor.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.121-131
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• 1999

There have been many questionnaires, catecholeamins analysis and bio-signal analysis to analyze human stress condition through out the years, and especially researches in bio-signal analysis have been actively increasing. The purpose of our research is Quantitative analysis of stress with synthesis of bio-signals. The stress status was estimated using the bio-signals and fuzzy theory which combines these signals and physiological knowledge. Stress was estimated by a 'coin-stacking' experiment with two type-relax and stress status. To do the experiment EMG, respiration, periphery temperature, heart rate and skin conductances were used to evaluate human stress stages. The system was tested to 10 healthy persons and achieved a template of a stress progress and stress variations were classified to 4 steps by continuous or rising status of stress progress.

• The Journal of Information Technology
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• v.9 no.1
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• pp.9-19
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• 2006

Medications or conitive-behavior methods have been mainly used as a treatment of alcoholism. lately the virtualy technology has been applied to the kink of alcoholic disorders. A virtual environment makes him having avility to over come the drink. In this study, we were implemented by making panorama images and 3D object modules using 3D MAX, VRML, JAVA. And the BAR stimulator that composed with a position sensor, head mount display, and audio system, is suggested. To illustrate the physiological difference between a person who has a alcoholism and without a liquor bottle, heart rate was measured during experiment, and also measured a person's HR after the virtual reality training. The system measures the Physiological signals such as ECG, Temperature, analyzes those data automatically. The system has two parts, one is physiological data acquisition part which gets the body signal, and the other one is mobile nuit which includes signal processing and transmission functions, And Bluetooth allows two parts to communicate with each other. we demonstrated the subjective effectiveness of virtual reality psychotherapy through the clinical experiment.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.289-292
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• 2000

선형 자귀회귀(AR) 모델을 근거로한 HRV 파워 스펙트럼해석은 비침습적으로 자율신경의 반응을 정량화하는데 폭넓게 사용된다. 본 연구는 단구간(2분 미만)의 심전도와 맥파 신호로부터 시계열 HRV의 파워스펙트럼을 추정한다. 시계열은 정상인을 대상으로 검출한 심전도와 맥파신호의 특징점 시간간격(RRI, PPI)으로부터 구하였다. 발생된 시계열은 다항식 보간법에 의해 AR모델에 적합하게 재구성하였으며, AR모델 계수는 Burg법에 의해 계산하였다. AR모델을 적용한 단구간의 심전도와 맥파의 심박변동에 대한 파워스펙트럼밀도는 저주파수(LF)와 고주파수(HF)에서 매끄러운 스펙트럼 파워를 나타내고 있다. 또한 동일한 피험자의 심전도와 맥파의 파워스펙트럼밀도를 비교한 결과 동일한 모양을 나타내었다.

• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.6
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• pp.668-673
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• 2014

Cold and Heat pattern identification(CHPI) in traditional East Asian medicine(TEAM) is one of the major indicator to distinguish characteristics of disease and to determine treatment method. Basic parameters to determine CHPI include the pulse rate, respiration rate, and pulse power. Studies to associate physiological responses of human body by cold stress(CS) with CHPI in TEAM were rarely done so far. This study aims to explore the effects of cold stress on pulse signal via a feasibility study for three subjects and investigate some indices which can reflect autonomic nerve reaction(ANR). We measured radial pulse signals and respiration signal of the investigated subjects before the CS, during the CS which continues for 5 minutes, and immediately after the CS, respectively. Finally, we analyzed the pulse rate (P), respiration rate (R), pulse power, pulse depth, and pulse rate per respiration (P/R ratio). As a result, the P/R ratio showed a consistently decreasing tendency through the CS stimulation process, while other parameters behaved more complex and in subject-specific ways. It implies that, among candidate parameters, the P/R ratio is a simple but the most probable parameter that can be used as the ANR indicator. This result is also consistent with the theory in TEAM scripts, in which the P/R ratio is predicted to be a direct indicator for the CHPI. This pilot study shows that P/R ratio can be more appropriately associated with the ANR than heart rate or respiration rate alone. Extensive studies will be necessary to verify or confirm the P/R ratio as an appropriate and well defined parameter for ANR.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.581-587
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• 2004

Conventional power spectrum methods based on FFT, AR method are not appropriate for analyzing biomedical signals whose spectral characteristics change rapidly. On the other hand, time-frequency analysis has more desirable characteristics of a time-varying spectrum. In this study, we investigated the spectral components of heart rate variability(HRV) in time-frequency domain using time frequency analysis methods. In the various time-frequency kernels functions, we studied the suitable kernels for the analysis of HRV using synthetic HRV signals. First, we evaluated the time/frequency resolution and cross term reduction of various kernel functions. Then, from the instantaneous frequency, obtained from time-frequency distribution, the method extracting frequency components of HRV was proposed. Subjects were 17 healthy young men. A coin-stacking task was used to induce mental stress. For each subjects, the experiment time was 3 minutes. Electrocardiogram, measured during the experiment, was analyzed after converted to HRV signal. In the results, emotional stress of subjects produced an increase in sympathetic activity. Sympathetic activation was responsible for the significant increase in the LF/HF ratio. Subjects were divided into two groups with task ability. Subjects who have higher mental stress have lack of task ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4021-4030
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• 2015

Pulse wave is the physiological responses through the autonomic nervous system such as ECG. It is relatively convenient because it can measure the signal just by applying a sensor on a finger. So, it can be usefully employed in the field of U-Healthcare. The objects of this study are acquiring the PPG (Photoplethysmography) one of the way of measuring the pulse waves in non-invasive way using the CMOS image sensor on a smartphone camera, developing the portable system judging stressful or not, and confirming the applicability in the field of u-Healthcare. PPG was acquired by using image data from smartphone camera without separate sensors and analyzed. Also, with that image signal data, HRV (Heart Rate Variability) and stress index were offered users by just using smartphone without separate host equipment. In addition, the reliability and accuracy of acquired data were improved by developing additional hardware device. From these experiments, we can confirm that measuring heart rate through the PPG, and the stress index for analysis the stress degree using the image of a smartphone camera are possible. In this study, we used a smartphone camera, not commercialized product or standardized sensor, so it has low resolution than those of using commercialized external sensor. However, despite this disadvantage, it can be usefully employed as the u-Healthcare device because it can obtain the promising data by developing additional external device for improvement reliability of result and optimization algorithm.